“Outside-in” versus “Inside-out” machine learning
It sounds like you are interested in the ideas of intrinsic motivation and attention in the context of machine learning. These are big topics, and the subject of much active research.

Intrinsic motivation says that the key to identifying interesting patterns and skills that are worth learning is to give the agent some intrinsic reason to learn to do new things. This is not dissimilar from what humans have: learning new things, and improving or exercising our capabilities to the fullest is what Aristotle identified as the good life. There are thus good reasons to think that intrinsic motivation for AI might solve the problem you identify. Current research in this domain is exploring different mathematical ways to represent intrinsic motivation.

Attention was the subject of a large burst of research in deep neural networks during the last few years. Here's a recent talk from AWS at ICML that provides a good overview. The idea here is that an agent can learn both a reasonable mapping from inputs to outputs for some problem, and a separate mapping that describes how different future inputs should "activate" certain parts of the input/output mapping that the agent has learned. Essentially attention-driven models include a second component that learns which features of the input to pay attention to when engaging in certain kinds of tasks.